U.S. patent application number 15/273136 was filed with the patent office on 2018-03-22 for method to allow for question and answer system to dynamically return different responses based on roles.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Donna K. Byron, Elie Feirouz, Daniel M. Jamrog, Kristin A. Witherspoon.
Application Number | 20180081934 15/273136 |
Document ID | / |
Family ID | 61620394 |
Filed Date | 2018-03-22 |
United States Patent
Application |
20180081934 |
Kind Code |
A1 |
Byron; Donna K. ; et
al. |
March 22, 2018 |
METHOD TO ALLOW FOR QUESTION AND ANSWER SYSTEM TO DYNAMICALLY
RETURN DIFFERENT RESPONSES BASED ON ROLES
Abstract
Embodiments are directed to a question and answer (QA) pipeline
system that adjusts answers to input questions based on a user
criteria, thus implementing a content-based determination of access
permissions. The QA system allows for information to be retrieved
based on permission granted to a user. Documents are ingested and
assigned an access level based on a defined information access
policy. The QA system is implemented with the defined information
access policy, the ingested documents, and the inferred access
levels. For the QA system implementation, a user enters a question;
primary search and answer extraction stages are performed;
candidate answer extraction is performed using only content the
user is allowed to access; the candidate answers are scored,
ranked, and merged; ranked answers based on user permissions are
filtered; and answers are provided to the user.
Inventors: |
Byron; Donna K.; (Petersham,
MA) ; Feirouz; Elie; (Lexington, MA) ; Jamrog;
Daniel M.; (Acton, MA) ; Witherspoon; Kristin A.;
(Somerville, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
61620394 |
Appl. No.: |
15/273136 |
Filed: |
September 22, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/40 20200101;
H04L 63/105 20130101; G06F 40/205 20200101; G06F 21/6245 20130101;
H04L 63/10 20130101; H04L 63/102 20130101; G06F 2221/2141 20130101;
G06F 40/30 20200101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; H04L 29/06 20060101 H04L029/06; G06F 17/27 20060101
G06F017/27 |
Claims
1. A computer implemented method for adjusting answers to questions
based on a user criteria by an information handling system capable
of answering questions, the system comprising a processor and a
memory comprising instructions executed by the processor, the
method comprising: receiving a question from a user with a user
profile comprising one or more of a responsibility role and a
permission level; deriving a content access constraint criteria
from the one or more of the responsibility role and the permission
level; applying natural language processing (NLP) and deep analytic
analysis to content restricted to the content access constraint
criteria to form an answer to the question; and providing to the
user a notification comprising the answer to the question.
2. The method of claim 1, wherein the permission level is based on
a subscription.
3. The method of claim 1, wherein the content access constraint
criteria controls access to embedded content in documents based on
a passage classifier model analysis including accumulated
discovery.
4. The method of claim 3, wherein a first responsibility role for a
first employee without management responsibility receives a more
restrictive content access constraint criteria than a second
employee with management responsibility.
5. The method of claim 1, wherein the content restricted to the
content access constraint criteria is based on an inferred access
level classified to the content.
6. The method of claim 5, wherein the inferred access level
classified to the content is one or more of manually tagged,
extracted based on NLP and deep analytic analysis, based on a
rules-based policy, or based on a classification-based policy.
7. The method of claim 1, wherein, in addition to the notification
comprising the answer to the question, the user is provided with an
indication of an access level of the answer being provided.
8. A computer program product for adjusting answers to questions
based on a user criteria by an information handling system capable
of answering questions, the computer program product comprising a
computer readable storage medium having program instructions
embodied therewith, the program instructions executable by a
processor to cause the processor to: receive a question from a user
with a user profile comprising one or more of a responsibility role
and a permission level; derive a content access constraint criteria
from the one or more of the responsibility role and the permission
level; apply natural language processing (NLP) and deep analytic
analysis to content restricted to the content access constraint
criteria to form an answer to the question; and provide to the user
a notification comprising the answer to the question.
9. The computer program product of claim 8, wherein the permission
level is based on a subscription.
10. The computer program product of claim 8, wherein the content
access constraint criteria controls access to embedded content in
documents based on a passage classifier model analysis including
accumulated discovery.
11. The computer program product of claim 10, wherein a first
responsibility role for a first employee without management
responsibility receives a more restrictive content access
constraint criteria than a second employee with management
responsibility.
12. The computer program product of claim 8, wherein the content
restricted to the content access constraint criteria is based on an
inferred access level classified to the content.
13. The computer program product of claim 12, wherein the inferred
access level classified to the content is one or more of manually
tagged, extracted based on NLP and deep analytic analysis, based on
a rules-based policy, or based on a classification-based
policy.
14. A system for adjusting answers to questions based on a user
criteria by an information handling system capable of answering
questions, the system comprising: a memory comprising executable
instructions; and a processor configured to execute the executable
instructions to: receive a question from a user with a user profile
comprising one or more of a responsibility role and a permission
level; derive a content access constraint criteria from the one or
more of the responsibility role and the permission level; apply
natural language processing (NLP) and deep analytic analysis to
content restricted to the content access constraint criteria to
form an answer to the question; and provide to the user a
notification comprising the answer to the question.
15. The system of claim 14, wherein the permission level is based
on a subscription.
16. The system of claim 14, wherein the content access constraint
criteria controls access to embedded content in documents based on
a passage classifier model analysis including accumulated
discovery.
17. The system of claim 16, wherein a first responsibility role for
a first employee without management responsibility receives a more
restrictive content access constraint criteria than a second
employee with management responsibility.
18. The system of claim 14, wherein the content restricted to the
content access constraint criteria is based on an inferred access
level classified to the content.
19. The system of claim 18, wherein the inferred access level
classified to the content is one or more of manually tagged,
extracted based on NLP and deep analytic analysis, based on a
rules-based policy, or based on a classification-based policy.
20. The system of claim 14, wherein, in addition to the
notification comprising the answer to the question, the user is
provided with an indication of an access level of the answer being
provided.
Description
BACKGROUND
[0001] Question and answer systems utilize the same corpus content
for all users when formulating answers. However, in many
information access scenarios, differential access to different
users is desired. For example, differential access may be preferred
for the following situations: if a user has paid for additional
and/or high value content, when confidential information is shared
within a company versus externally-visible information, and when
special information (e.g., security clearance information, health
care information, etc.) access is granted to certain individuals.
When differential access is assigned in current question and answer
systems, access privileges are typically assigned at a file-space
permissions level or by static user profiles matched to
document-level metadata such as tagging particular documents with
appropriate tags (e.g., "company confidential" tags). Such methods
are manually determined and are time consuming.
[0002] Thus, an improved question and answer system that allows for
differential access for different users is desired.
SUMMARY
[0003] Embodiments are directed to a computer-implemented method, a
computer program product, and a system for adjusting answers to
questions based on a user criteria.
[0004] In an embodiment, the computer-implemented method is
implemented in a system capable of answering questions, the system
comprising a processor and a memory comprising instructions
executed by the processor.
[0005] In an embodiment, the computer program product comprises a
computer readable storage medium having program instructions
embodied therewith, the program instructions executable by a
processor.
[0006] In an embodiment, the system comprises a processor and a
memory, which comprises instructions executed by the processor.
[0007] In an embodiment, the processor executes the steps of:
receiving a question from a user with a user profile comprising one
or more of a responsibility role and a permission level; deriving a
content access constraint criteria from the one or more of the
responsibility role and the permission level; applying natural
language processing (NLP) and deep analytic analysis to content
restricted to the content access constraint criteria to form an
answer to the question; and providing to the user a notification
comprising the answer to the question.
[0008] Additional features and advantages are apparent from the
following detailed description that proceeds with reference to the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The foregoing and other aspects of the present invention are
best understood from the following detailed description when read
in connection with the accompanying drawings. For the purpose of
illustrating the invention, there is shown in the drawings
embodiments that are presently preferred, it being understood,
however, that the invention is not limited to the specific
instrumentalities disclosed. Included in the drawings are the
following Figures:
[0010] FIG. 1 depicts a schematic diagram of an embodiment of a
cognitive system implementing a question and answer (QA) generation
system in a computer network;
[0011] FIG. 2 illustrates a QA system pipeline, of a cognitive
system, for processing an input question, according to an
embodiment;
[0012] FIG. 3 is a flow diagram illustrating stages for adjusting
answers to questions based on a user criteria in a QA system,
according to an embodiment;
[0013] FIG. 4 is a flowchart of a method for adjusting answers to
questions based on a user criteria in a QA system, in accordance
with an embodiment; and
[0014] FIG. 5 is a block diagram of an example data processing
system in which aspects of the illustrative embodiments are
implemented.
DETAILED DESCRIPTION
[0015] Embodiments are directed to content-based determination of
access permissions for a question and answer system. By allowing
for different ingested content to be used based on user privileges
and/or user criteria, flexibility is introduced into a question and
answer system. Moreover, paid-content providers are able to
incentivize customers to upgrade to higher paid service levels.
According to embodiments herein, sensitive information in documents
can be protected without the need to manually tag more restrictive
access levels by system administrators.
[0016] The present description and claims may make use of the terms
"a," "at least one of," and "one or more of," with regard to
particular features and elements of the illustrative embodiments.
It should be appreciated that these terms and phrases are intended
to state that there is at least one of the particular feature or
element present in the particular illustrative embodiment, but that
more than one can also be present. That is, these terms/phrases are
not intended to limit the description or claims to a single
feature/element being present or require that a plurality of such
features/elements be present. To the contrary, these terms/phrases
only require at least a single feature/element with the possibility
of a plurality of such features/elements being within the scope of
the description and claims.
[0017] In addition, it should be appreciated that the following
description uses a plurality of various examples for various
elements of the illustrative embodiments to further illustrate
example implementations of the illustrative embodiments and to aid
in the understanding of the mechanisms of the illustrative
embodiments. These examples are intended to be non-limiting and are
not exhaustive of the various possibilities for implementing the
mechanisms of the illustrative embodiments. It will be apparent to
those of ordinary skill in the art in view of the present
description that there are many other alternative implementations
for these various elements that may be utilized in addition to, or
in replacement of, the example provided herein without departing
from the spirit and scope of the present invention.
[0018] As an overview, a cognitive system is a specialized computer
system, or set of computer systems, configured with hardware and/or
software logic (in combination with hardware logic upon which the
software executes) to emulate human cognitive functions. These
cognitive systems apply human-like characteristics to conveying and
manipulating ideas which, when combined with the inherent strengths
of digital computing, can solve problems with high accuracy and
resilience on a large scale. IBM Watson.TM. is an example of one
such cognitive system which can process human readable language and
identify inferences between text passages with human-like accuracy
at speeds far faster than human beings and on a much larger scale.
In general, such cognitive systems are able to perform the
following functions: [0019] Navigate the complexities of human
language and understanding [0020] Ingest and process vast amounts
of structured and unstructured data [0021] Generate and evaluate
hypotheses [0022] Weigh and evaluate responses that are based only
on relevant evidence [0023] Provide situation-specific advice,
insights, and guidance [0024] Improve knowledge and learn with each
iteration and interaction through machine learning processes [0025]
Enable decision making at the point of impact (contextual guidance)
[0026] Scale in proportion to the task [0027] Extend and magnify
human expertise and cognition [0028] Identify resonating,
human-like attributes and traits from natural language [0029]
Deduce various language specific or agnostic attributes from
natural language [0030] High degree of relevant recollection from
data points (images, text, voice) (memorization and recall) [0031]
Predict and sense with situation awareness that mimics human
cognition based on experiences [0032] Answer questions based on
natural language and specific evidence
[0033] In one aspect, cognitive systems provide mechanisms for
answering questions posed to these cognitive systems using a
Question Answering pipeline or system (QA system). The QA pipeline
or system is an artificial intelligence application executing on
data processing hardware that answers questions pertaining to a
given subject-matter domain presented in natural language. The QA
pipeline receives inputs from various sources including input over
a network, a corpus of electronic documents or other data, data
from a content creator, information from one or more content users,
and other such inputs from other possible sources of input. Data
storage devices store the corpus of data. A content creator creates
content in a document for use as part of a corpus of data with the
QA pipeline. The document may include any file, text, article, or
source of data for use in the QA system. For example, a QA pipeline
accesses a body of knowledge about the domain, or subject matter
area (e.g., financial domain, medical domain, legal domain, etc.)
where the body of knowledge (knowledgebase) can be organized in a
variety of configurations, e.g., a structured repository of
domain-specific information, such as ontologies, or unstructured
data related to the domain, or a collection of natural language
documents about the domain.
[0034] Content users input questions to the cognitive system which
implements the QA pipeline. The QA pipeline then answers the input
questions using the content in the corpus of data by evaluating
documents, sections of documents, portions of data in the corpus,
or the like. When a process evaluates a given section of a document
for semantic content, the process can use a variety of conventions
to query such document from the QA pipeline, e.g., sending the
query to the QA pipeline as a well-formed question which is then
interpreted by the QA pipeline and a response is provided
containing one or more answers to the question. Semantic content is
content based on the relation between signifiers, such as words,
phrases, signs, and symbols, and what they stand for, their
denotation, or connotation. In other words, semantic content is
content that interprets an expression, such as by using natural
language processing.
[0035] As will be described in greater detail hereafter, the QA
pipeline receives an input question, parses the question to extract
the major features of the question, uses the extracted features to
formulate queries, and then applies those queries to the corpus of
data. Based on the application of the queries to the corpus of
data, the QA pipeline generates a set of hypotheses, or candidate
answers to the input question, by looking across the corpus of data
for portions of the corpus of data that have some potential for
containing a valuable response to the input question. The QA
pipeline then performs deep analysis on the language of the input
question and the language used in each of the portions of the
corpus of data found during the application of the queries using a
variety of reasoning algorithms. There may be hundreds or even
thousands of reasoning algorithms applied, each of which performs
different analysis, e.g., comparisons, natural language analysis,
lexical analysis, or the like, and generates a score. For example,
some reasoning algorithms may look at the matching of terms and
synonyms within the language of the input question and the found
portions of the corpus of data. Other reasoning algorithms may look
at temporal or spatial features in the language, while others may
evaluate the source of the portion of the corpus of data and
evaluate its veracity.
[0036] The scores obtained from the various reasoning algorithms
indicate the extent to which the potential response is inferred by
the input question based on the specific area of focus of that
reasoning algorithm. Each resulting score is then weighted against
a statistical model. The statistical model captures how well the
reasoning algorithm performed at establishing the inference between
two similar passages for a particular domain during the training
period of the QA pipeline. The statistical model is used to
summarize a level of confidence that the QA pipeline has regarding
the evidence that the potential response, i.e., candidate answer,
is inferred by the question. This process is repeated for each of
the candidate answers until the QA pipeline identifies candidate
answers that surface as being significantly stronger than others
and thus generates a final answer, or ranked set of answers, for
the input question.
[0037] As mentioned above, QA pipeline and mechanisms operate by
accessing information from a corpus of data or information (also
referred to as a corpus of content), analyzing it, and then
generating answer results based on the analysis of this data.
Accessing information from a corpus of data typically includes: a
database query that answers questions about what is in a collection
of structured records, and a search that delivers a collection of
document links in response to a query against a collection of
unstructured data (text, markup language, etc.). Conventional
question answering systems are capable of generating answers based
on the corpus of data and the input question, verifying answers to
a collection of questions for the corpus of data, correcting errors
in digital text using a corpus of data, and selecting answers to
questions from a pool of potential answers, i.e., candidate
answers.
[0038] Content creators, such as article authors, electronic
document creators, web page authors, document database creators,
and the like, determine use cases for products, solutions, and
services described in such content before writing their content.
Consequently, the content creators know what questions the content
is intended to answer in a particular topic addressed by the
content. Categorizing the questions, such as in terms of roles,
type of information, tasks, or the like, associated with the
question, in each document of a corpus of data allows the QA
pipeline to more quickly and efficiently identity documents
containing content related to a specific query. The content may
also answer other questions that the content creator did not
contemplate that may be useful to content users. The questions and
answers may be verified by the content creator to be contained in
the content for a given document. These capabilities contribute to
improved accuracy, system performance, machine learning, and
confidence of the QA pipeline. Content creators, automated tools,
or the like, annotate or otherwise generate metadata for providing
information useable by the QA pipeline to identify question and
answer attributes of the content.
[0039] Operating on such content, the QA pipeline generates answers
for input questions using a plurality of intensive analysis
mechanisms which evaluate the content to identify the most probable
answers, i.e., candidate answers, for the input question. The most
probable answers are output as a ranked listing of candidate
answers ranked according to their relative scores or confidence
measures calculated during evaluation of the candidate answers, as
a single final answer having a highest ranking score or confidence
measure, or which is a best match to the input question, or a
combination of ranked listing and final answer.
[0040] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a cognitive system 100 implementing a question and
answer (QA) pipeline 108 in a computer network 102. One example of
a question/answer generation operation which may be used in
conjunction with the principles described herein is described in
U.S. Patent Application Publication No. 2011/0125734, which is
herein incorporated by reference in its entirety. The cognitive
system 100 is implemented on one or more computing devices 104
(comprising one or more processors and one or more memories, and
potentially any other computing device elements generally known in
the art including buses, storage devices, communication interfaces,
and the like) connected to the computer network 102. The network
102 includes multiple computing devices 104 in communication with
each other and with other devices or components via one or more
wired and/or wireless data communication links, where each
communication link comprises one or more of wires, routers,
switches, transmitters, receivers, or the like. The cognitive
system 100 and network 102 enables question/answer (QA) generation
functionality for one or more cognitive system users via their
respective computing devices. Other embodiments of the cognitive
system 100 may be used with components, systems, sub-systems,
and/or devices other than those that are depicted herein.
[0041] The cognitive system 100 is configured to implement a QA
pipeline 108 that receives inputs from various sources. For
example, the cognitive system 100 receives input from the network
102, a corpus of electronic documents 140, cognitive system users,
and/or other data and other possible sources of input. In one
embodiment, some or all of the inputs to the cognitive system 100
are routed through the network 102. The various computing devices
104 on the network 102 include access points for content creators
and QA system users. Some of the computing devices 104 include
devices for a database storing the corpus of data 140. Portions of
the corpus of data 140 may also be provided on one or more other
network attached storage devices, in one or more databases, or
other computing devices not explicitly shown in FIG. 1. The network
102 includes local network connections and remote connections in
various embodiments, such that the cognitive system 100 may operate
in environments of any size, including local and global, e.g., the
Internet.
[0042] In one embodiment, the content creator creates content in a
document of the corpus of data 140 for use as part of a corpus of
data with the cognitive system 100. The document includes any file,
text, article, or source of data for use in the cognitive system
100. QA system users access the cognitive system 100 via a network
connection or an Internet connection to the network 102, and input
questions to the cognitive system 100 that are answered by the
content in the corpus of data 140. In one embodiment, the questions
are formed using natural language. The cognitive system 100 parses
and interprets the question via a QA pipeline 108, and provides a
response to the cognitive system user containing one or more
answers to the question. In some embodiments, the cognitive system
100 provides a response to users in a ranked list of candidate
answers while in other illustrative embodiments, the cognitive
system 100 provides a single final answer or a combination of a
final answer and ranked listing of other candidate answers.
[0043] The cognitive system 100 implements the QA pipeline 108
which comprises a plurality of stages for processing an input
question and the corpus of data 140. The QA pipeline 108 generates
answers for the input question based on the processing of the input
question and the corpus of data 140. The QA pipeline 108 is
described in greater detail with regard to FIG. 2.
[0044] In some illustrative embodiments, the cognitive system 100
may be the IBM Watson.TM. cognitive system available from
International Business Machines Corporation of Armonk, N.Y., which
is augmented with the mechanisms of the illustrative embodiments
described hereafter. As outlined previously, a QA pipeline of the
IBM Watson.TM. cognitive system receives an input question, which
it then parses to extract the major features of the question, and
which in turn are then used to formulate queries that are applied
to the corpus of data. Based on the application of the queries to
the corpus of data, a set of hypotheses, or candidate answers to
the input question, are generated by looking across the corpus of
data for portions of the corpus of data that have some potential
for containing a valuable response to the input question. The QA
pipeline of the IBM Watson.TM. cognitive system then performs deep
analysis on the language of the input question and the language
used in each of the portions of the corpus of data found during the
application of the queries using a variety of reasoning algorithms.
The scores obtained from the various reasoning algorithms are then
weighted against a statistical model that summarizes a level of
confidence that the QA pipeline of the IBM Watson.TM. cognitive
system has regarding the evidence that the potential response,
i.e., candidate answer, is inferred by the question. This process
is repeated for each of the candidate answers to generate a ranked
listing of candidate answers which may then be presented to the
user that submitted the input question, or from which a final
answer is selected and presented to the user. More information
about the QA pipeline of the IBM Watson.TM. cognitive system may be
obtained, for example, from the IBM Corporation website, IBM
Redbooks, and the like. For example, information about the QA
pipeline of the IBM Watson.TM. cognitive system can be found in
Yuan et al., "Watson and Healthcare." IBM developerWorks, 2011 and
"The Era of Cognitive Systems: An Inside Look at IBM Watson and How
it Works" by Rob High, IBM Redbooks, 2012.
[0045] As shown in FIG. 1, in accordance with some illustrative
embodiments, the cognitive system 100 is further augmented, in
accordance with the mechanisms of the illustrative embodiments, to
include logic implemented in specialized hardware, software
executed on hardware, or any combination of specialized hardware
and software executed on hardware.
[0046] Results from the corpus 140 are stored in storage device 150
associated with either the cognitive system 100, where the storage
device 150 may be a memory, a hard disk based storage device, flash
memory, solid state storage device, or the like (hereafter assumed
to be a "memory" with in-memory representations of the acyclic
graphs for purposes of description).
[0047] FIG. 2 illustrates a QA system pipeline 108, of a cognitive
system, for processing an input question. The QA system pipeline
108 of FIG. 2 may be implemented, for example, as QA pipeline 108
of cognitive system 100 in FIG. 1. It should be appreciated that
the stages as shown in FIG. 2 are implemented as one or more
software engines, components, or the like, which are configured
with logic for implementing the functionality attributed to the
particular stage. Each stage is implemented using one or more of
such software engines, components or the like. The software
engines, components, etc., are executed on one or more processors
of one or more data processing systems or devices and utilize or
operate on data stored in one or more data storage devices,
memories, or the like, on one or more of the data processing
systems. Additional stages may be provided to implement the
improved mechanism, or separate logic from the pipeline 108 may be
provided for interfacing with the pipeline 108 and implementing the
improved functionality and operations of the illustrative
embodiments provided herein.
[0048] As shown in FIG. 2, the QA pipeline 108 comprises a
plurality of stages 205-250 through which the cognitive system
operates to analyze an input question and generate a final
response. According to embodiments herein, the QA pipeline 108
operates to adjust answers to input questions based on a user
criteria, thus implementing a content-based determination of access
permissions.
[0049] In an initial question input stage 205, the QA pipeline 108
receives an input question that is presented in a natural language
format. According to an embodiment, the input question is inputted
by a user with a user profile that includes one or more of a
responsibility role and a permission level. That is, a user inputs,
via a user interface, an input question for which the user wishes
to obtain an answer, e.g., "Who are Washington's closest
advisors?"
[0050] In response to receiving the input question, the next stage
of the QA pipeline 108, i.e., content access constraint criteria
determination stage 210, derives from the one or more of the
responsibility role and the permission level a content access
constraint criteria for the input question. The content access
constraint criteria is utilized to restrict content based on the
user's responsibility role and/or permission level.
[0051] The next stage of the QA pipeline 108, i.e., the question
and topic analysis stage 215, parses the input question using
natural language processing (NLP) techniques to extract major
features from the input question, and classify the major features
according to types, e.g., names, dates, or any of a plethora of
other defined topics. For example, in the example question above,
the term "who" may be associated with a topic for "persons"
indicating that the identity of a person is being sought,
"Washington" may be identified as a proper name of a person with
which the question is associated, "closest" may be identified as a
word indicative of proximity or relationship, and "advisors" may be
indicative of a noun or other language topic.
[0052] In addition, the extracted major features include key words
and phrases classified into question characteristics, such as the
focus of the question, the lexical answer type (LAT) of the
question, and the like. As referenced to herein, a lexical answer
type (LAT) is a word in, or a word inferred from, the input
question that indicates the type of the answer, independent of
assigning semantics to that word. For example, in the question
"What maneuver was invented in the 1500s to speed up the game and
involves two pieces of the same color?" the LAT is the string
"maneuver." The focus of a question is the part of the question
that, if replaced by the answer, makes the question a standalone
statement. For example, in the question "What drug has been shown
to relieve the symptoms of ADD with relatively few side effects?,"
the focus is "What drug" since this phrase can be replaced with the
answer, e.g., "Adderall," to generate the sentence "Adderall has
been shown to relieve the symptoms of ADD with relatively few side
effects." The focus often, but not always, contains the LAT. On the
other hand, in many cases it is not possible to infer a meaningful
LAT from the focus.
[0053] Referring again to FIG. 2, the identified major features are
then used during the question decomposition stage 220 to decompose
the question into one or more queries that are applied to the
corpora of data/information 255 in order to generate one or more
hypotheses. The queries are generated in any known or later
developed query language, such as the Structure Query Language
(SQL), or the like. The queries are applied to one or more
databases storing information about the electronic texts,
documents, articles, websites, and the like, that make up the
corpora of data/information 255. That is, these various sources
themselves, different collections of sources, and the like,
represent a different corpus 257 within the corpora 255. There may
be different corpora 257 defined for different collections of
documents based on various criteria depending upon the particular
implementation. For example, different corpora may be established
for different topics, subject matter categories, sources of
information, or the like. As one example, a first corpus may be
associated with healthcare documents while a second corpus may be
associated with financial documents. Alternatively, one corpus may
be documents published by the U.S. Department of Energy while
another corpus may be IBM Redbooks documents. Any collection of
content having some similar attribute may be considered to be a
corpus 257 within the corpora 255.
[0054] The queries are applied to one or more databases storing
information about the electronic texts, documents, articles,
websites, and the like, that make up the corpus of
data/information, e.g., the corpus of data 140 in FIG. 1. The
queries are applied to the corpus of data/information at hypothesis
generation stage 225 to generate results identifying potential
hypotheses for answering the input question, which can then be
evaluated. That is, the application of the queries results in the
extraction of portions of the corpus of data/information matching
the criteria of the particular query. These portions of the corpus
are then analyzed and used, during the hypothesis generation stage
225, to generate hypotheses for answering the input question. These
hypotheses are also referred to herein as "candidate answers" for
the input question. For any input question, at this stage 225,
there may be hundreds of hypotheses or candidate answers generated
that may need to be evaluated.
[0055] At the next stage, content access constraint criteria
application stage 230, the responsibility role and/or the
permission level associated with the user of the input question is
applied to the candidate answers generated at stage 225. That is,
the determined content access constraint criteria is used to block
or allow (i.e., filter) answers. According to an embodiment, for
each candidate answer received, an access level is inferred. If a
candidate answer is manually tagged with a particular access level,
that particular access level is utilized. Deep NLP/feature
extraction may be performed on each text segment to infer the
access level. In another embodiment, a rules-based policy or
classification-based policy is applied to determine an access
level. Once the access levels for the candidate answers are
determined, they are compared to the user's content access
constraint criteria. Answers with an access level higher than that
assigned to the user, as identified by the content access
constraint criteria, are removed from the pool of candidate
answers. Similarly, if one or more particular text segments (e.g.,
passage, subsection, paragraph, chapter, article, and the like) of
a candidate answer has an access level that conflicts with that of
the user, that one or more particular text segment may be extracted
while a remaining portion of the candidate answer is available.
[0056] The QA pipeline 108, in stage 235, then performs a deep
analysis and comparison of the language of the input question and
the language of each hypothesis or "candidate answer" filtered by
the previous stage 230, as well as performs evidence scoring to
evaluate the likelihood that the particular hypothesis is a correct
answer for the input question. As described in FIG. 1, this
involves using a plurality of reasoning algorithms, each performing
a separate type of analysis of the language of the input question
and/or content of the corpus that provides evidence in support of,
or not in support of, the hypothesis. Each reasoning algorithm
generates a score based on the analysis it performs which indicates
a measure of relevance of the individual portions of the corpus of
data/information extracted by application of the queries as well as
a measure of the correctness of the corresponding hypothesis, i.e.,
a measure of confidence in the hypothesis. There are various ways
of generating such scores depending upon the particular analysis
being performed. In general, however, these algorithms look for
particular terms, phrases, or patterns of text that are indicative
of terms, phrases, or patterns of interest and determine a degree
of matching with higher degrees of matching being given relatively
higher scores than lower degrees of matching.
[0057] In an embodiment, the content access constraint criteria
application may be performed at this later stage. That is, the
responsibility role and/or the permission level associated with the
user of the input question is applied to the analyzed and scored
answers generated at stage 235.
[0058] In the synthesis stage 240, the large number of scores
generated by the various reasoning algorithms are synthesized into
confidence scores or confidence measures for the various
hypotheses. This process involves applying weights to the various
scores, where the weights have been determined through training of
the statistical model employed by the QA pipeline 108 and/or
dynamically updated. For example, the weights for scores generated
by algorithms that identify exactly matching terms and synonyms may
be set relatively higher than other algorithms that are evaluating
publication dates for evidence passages. The weights themselves may
be specified by subject matter experts or learned through machine
learning processes that evaluate the significance of
characteristics evidence passages and their relative importance to
overall candidate answer generation.
[0059] The weighted scores are processed in accordance with a
statistical model generated through training of the QA pipeline 108
that identifies a manner by which these scores may be combined to
generate a confidence score or measure for the individual
hypotheses or candidate answers. This confidence score or measure
summarizes the level of confidence that the QA pipeline 108 has
about the evidence that the candidate answer is inferred by the
input question, i.e., that the candidate answer is the correct
answer for the input question.
[0060] The resulting confidence scores or measures are processed by
a final confidence merging and ranking stage 245 which compares the
confidence scores and measures to each other, compares them against
predetermined thresholds, or performs any other analysis on the
confidence scores to determine which hypotheses/candidate answers
are the most likely to be the correct answer to the input question.
The hypotheses/candidate answers are ranked according to these
comparisons to generate a ranked listing of hypotheses/candidate
answers (hereafter simply referred to as "candidate answers"). From
the ranked listing of candidate answers, at stage 250, a final
answer and confidence score, or final set of candidate answers and
confidence scores, are generated and output to the submitter of the
original input question via a graphical user interface (GUI) or
other mechanism for outputting information.
[0061] According to an embodiment herein, ranked answers may be
filtered based on user permissions. For example, answers to a user
may be shown depending on the user's permission level and/or
responsibility role, e.g., show answers to user if user has access
to all content or show a certain percentage of answers based on the
user's permission level and/or responsibility role.
[0062] According to an embodiment, along with the final set of
candidate answers and confidence scores, where the candidate
answers or portions thereof are restricted to reflect the access
level of the user, evidence of the adjusted answers may be provided
to the user via the GUI or other mechanism. The evidence may
include, for example, an indication of access levels of the answers
being provided. Moreover, areas where content is redacted may be
indicated, and teasers (i.e., a portion of redacted content) may be
shown.
[0063] In an embodiment, and with reference to FIG. 3, a flow
diagram 300 illustrates the stages for adjusting answers to
questions based on a user criteria in a QA system, such as the QA
system pipeline 108. First, the initial information access policy
is defined at stage 310. The information access policy refers to
how content is marked and identified to be assigned an access
level. In an embodiment, individual documents may be tagged with
access levels; such manually-tagged permissions are taken as
supervised training examples for the QA system 108. In another
embodiment, a rules engine may be utilized to record access policy
rules. For example, a rule may specify that managers are allowed to
view employee salaries of employees within their organization, or
that salesman are allowed to view deals, terms, and project
durations for contracts established within their territory of
responsibility. In yet another embodiment, access rights may be
described in terms of individual annotations or thresholds based on
items that are output from deep NLP analysis of the documents. For
example, free website members may view portions of documents with
no more than N analysis statements or no more than X geopolitical
entities.
[0064] The second stage is document ingestion and inferred access
level classification 320. At ingestion time, manually tagged
documents are sent for deep NLP processing. A model characterizing
documents for each access level may be generated by extracting
features from manually-tagged documents. Features of the inferred
access level classification process include, but are not limited
to: particular entity types, relation types, and predicates with
the document content; indicating words such as section headers,
document titles, or other metadata features; particular annotations
produced by document analysis identifying high-value content
determined by the domain, such as, for example, stock analysis,
intelligence, insights, corporate product comparisons, or market
analysis.
[0065] Stage 330 comprises implementing the question-answer system
(e.g., QA system pipeline 108), as described above with reference
to FIG. 2, with the defined information access policy, the ingested
documents, and the inferred access level classifications. To
summarize the QA system implementation: a user enters a question;
primary search and answer extraction stages are performed to block
or allow primary search retrieved content; candidate answer
extraction is performed using only content the user is allowed to
access; all content or only content allowed to be accessed by the
user is utilized to score, rank, and merge candidate answers;
ranked answers are filtered based on user permissions; and answers
plus evidence are sent to the user.
[0066] Stage 340 comprises the document lifecycle. As documents are
modified (i.e., re-ingesting a particular document), the document
may be erroneously marked as globally accessible. Thus, according
to an embodiment herein, documents may be analyzed and, depending
on the configuration, the access level may be updated or the
document may be flagged for human review.
[0067] Stage 350 comprises analytics. According to an embodiment,
the QA system 108 is able to report the number of user queries that
cannot be answered based on current access level. This information
may be used to suggest or recommend a different number of access
levels, for example. In another example of analytics, it may be
useful in a ground truth collection tool to indicate the access
level of the users performing the populating of the content. The
access levels of these users may be modulated to include the corpus
coverage they are able to annotate.
[0068] FIG. 4 is a flowchart 400 of a method for adjusting answers
to questions based on a user criteria in a QA system, such as the
QA system pipeline 108, in accordance with an embodiment.
[0069] At 410, a question from a user is received. The user has a
user profile comprising one or more of a responsibility role and a
permission level. In an embodiment, the permission level may be
based on a subscription of the user; for example, a subscription to
a particular magazine or newspaper.
[0070] At 420, a content access constraint criteria is derived from
the one or more of the responsibility role and the permission
level. In an embodiment, the content access constraint criteria
controls access to embedded content in documents based on a passage
classifier model analysis including accumulated discovery. In an
embodiment, a first responsibility role for a first employee
without management responsibility receives a more restrictive
content access constraint criteria than a second employee with
management responsibility.
[0071] At 430, natural language processing (NLP) and deep analytic
analysis is applied to content restricted to the content access
constraint criteria to form an answer to the question.
[0072] At 440, the user is provided with a notification comprising
the answer to the question.
[0073] According to embodiments provided herein, access permissions
are dynamically determined, thus allowing more flexibility of
access of content. The question and answer system and methods
provided herein that allow for differential access for different
users have several advantages: less time is required from the
corpus administrator to manually tag documents; more levels of
access permissions and/or a variety of policy controls are
encouraged to be established to reflect an organization's desired
information access; document access can be modified over time as
the document content changes; content control can be partitioned at
a finer grained level such as by paragraph, sentence, or individual
facts; the restriction on a document that human administrators
neglected to protect can be upgraded (such as a document with
information that should be restricted to company-confidential
level, but which was not tagged that way by the document
author).
[0074] An example use case is as follows: a publication displays
different data if a customer is currently a paid subscriber. A
non-subscriber receives a summary of the requested information,
potentially with a teaser such as "we have more answers available
for gold level subscription," and a paid subscriber receives
detailed requested information. This provides an encouragement for
more paid users to join. In another example, an employee website
returns information based on whether the employee is a manager or
non-manager. In yet another example, an employee website for a
government project contains confidential and non-confidential data.
Users permitted to see confidential data are allowed to access data
not permitted for users granted this type of access. Of course,
depending on the use case, administrators may wish to allow or
disallow inferred document access levels. In an example embodiment,
the QA system 108 may be configured to only UPGRADE (tighten) the
document's access level and never DOWNGRADE (loosen) access
constraints.
[0075] In an example workflow for the question and answer system
and methods provided herein that allow for differential access for
different users, the system may validate whether the
manually-assigned access level fits the document content.
Additionally, the system may recommend a number of access levels
based on various factors, such as number of users, type of
organization, and type and amount of content (e.g., an organization
may need five different sensitivity levels instead of the current
three). Moreover, the system allows for a company to not manually
tag every document; instead, a training corpus with levels may be
provided, and content is automatically tagged. Documents that are
desired to be sent to external customers may be analyzed so that
confidential content is redacted or removed, and the remaining
content can be sent externally.
[0076] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0077] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a head disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0078] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network (LAN), a wide area network (WAN) and/or a
wireless network. The network may comprise copper transmission
cables, optical transmission fibers, wireless transmission,
routers, firewalls, switches, gateway computers, and/or edge
servers. A network adapter card or network interface in each
computing/processing device receives computer readable program
instructions from the network and forwards the computer readable
program instructions for storage in a computer readable storage
medium within the respective computing/processing device.
[0079] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object-oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer, or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including LAN or WAN, or the connection may be made to
an external computer (for example, through the Internet using an
Internet Service Provider). In some embodiments, electronic
circuitry including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0080] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatuses (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0081] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0082] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operations steps to
be performed on the computer, other programmable apparatus, or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0083] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical functions. In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0084] FIG. 5 is a block diagram of an example data processing
system 500 in which aspects of the illustrative embodiments are
implemented. Data processing system 500 is an example of a
computer, such as a server or client, in which computer usable code
or instructions implementing the process for illustrative
embodiments are located. In one embodiment, FIG. 5 represents a
server computing device, such as a server, which implements the
cognitive system 100 described herein.
[0085] In the depicted example, data processing system 500 can
employ a hub architecture including a north bridge and memory
controller hub (NB/MCH) 501 and south bridge and input/output (I/O)
controller hub (SB/ICH) 502. Processing unit 503, main memory 504,
and graphics processor 505 can be connected to the NB/MCH 501.
Graphics processor 505 can be connected to the NB/MCH 501 through,
for example, an accelerated graphics port (AGP).
[0086] In the depicted example, a network adapter 506 connects to
the SB/ICH 502. An audio adapter 507, keyboard and mouse adapter
508, modem 509, read only memory (ROM) 505, hard disk drive (HDD)
511, optical drive (e.g., CD or DVD) 512, universal serial bus
(USB) ports and other communication ports 513, and PCI/PCIe devices
514 may connect to the SB/ICH 502 through bus system 516. PCI/PCIe
devices 514 may include Ethernet adapters, add-in cards, and PC
cards for notebook computers. ROM 505 may be, for example, a flash
basic input/output system (BIOS). The HDD 511 and optical drive 512
can use an integrated drive electronics (IDE) or serial advanced
technology attachment (SATA) interface. A super I/O (SIO) device
515 can be connected to the SB/ICH 502.
[0087] An operating system can run on processing unit 503. The
operating system can coordinate and provide control of various
components within the data processing system 500. As a client, the
operating system can be a commercially available operating system.
An object-oriented programming system, such as the Java programming
system, may run in conjunction with the operating system and
provide calls to the operating system from the object-oriented
programs or applications executing on the data processing system
500. As a server, the data processing system 500 can be an IBM.RTM.
eServer.TM. System p running the Advanced Interactive Executive
operating system or the Linux operating system. The data processing
system 500 can be a symmetric multiprocessor (SMP) system that can
include a plurality of processors in the processing unit 503.
Alternatively, a single processor system may be employed.
[0088] Instructions for the operating system, the object-oriented
programming system, and applications or programs are located on
storage devices, such as the HDD 511, and are loaded into the main
memory 504 for execution by the processing unit 503. The processes
for embodiments of the question and answer system pipeline 108,
described herein, can be performed by the processing unit 503 using
computer usable program code, which can be located in a memory such
as, for example, main memory 504, ROM 505, or in one or more
peripheral devices.
[0089] A bus system 516 can be comprised of one or more busses. The
bus system 516 can be implemented using any type of communication
fabric or architecture that can provide for a transfer of data
between different components or devices attached to the fabric or
architecture. A communication unit such as the modem 509 or the
network adapter 506 can include one or more devices that can be
used to transmit and receive data.
[0090] Those of ordinary skill in the art will appreciate that the
hardware depicted in FIG. 5 may vary depending on the
implementation. Other internal hardware or peripheral devices, such
as flash memory, equivalent non-volatile memory, or optical disk
drives may be used in addition to or in place of the hardware
depicted. Moreover, the data processing system 500 can take the
form of any of a number of different data processing systems,
including but not limited to, client computing devices, server
computing devices, tablet computers, laptop computers, telephone or
other communication devices, personal digital assistants, and the
like. Essentially, data processing system 500 can be any known or
later developed data processing system without architectural
limitation.
[0091] The system and processes of the figures are not exclusive.
Other systems, processes, and menus may be derived in accordance
with the principles of embodiments described herein to accomplish
the same objectives. It is to be understood that the embodiments
and variations shown and described herein are for illustration
purposes only. Modifications to the current design may be
implemented by those skilled in the art, without departing from the
scope of the embodiments. As described herein, the various systems,
subsystems, agents, managers, and processes can be implemented
using hardware components, software components, and/or combinations
thereof. No claim element herein is to be construed under the
provisions of 35 U.S.C. 112(f) unless the element is expressly
recited using the phrase "means for."
[0092] Although the invention has been described with reference to
exemplary embodiments, it is not limited thereto. Those skilled in
the art will appreciate that numerous changes and modifications may
be made to the preferred embodiments of the invention and that such
changes and modifications may be made without departing from the
true spirit of the invention. It is therefore intended that the
appended claims be construed to cover all such equivalent
variations as fall within the true spirit and scope of the
invention.
* * * * *